Conventional approaches to mapping and identifying expert resources suffers from one or more drawbacks. For example, in large organizations, the number of users and volume of content can make mapping and identifying expert resources problematic. Models of an organization's expert resources may be unable to factor in all of the available data and differentiate between users with a similar background or level of expertise. Maintaining an up-to-date model is also often difficult as people constantly acquire new knowledge and shift focus between different specializations. These problems are further exacerbated in multi-national organizations where the use of different languages makes mapping and identifying expert resources across different regions challenging. Furthermore, even when the ideal resource is identified, the identified individual may not be available or incentivized to respond. In some cases, an answer to a question already exists within the enterprise, but the existence of the question and answer is not readily known to the user. These and other drawbacks exist with conventional solutions.